摘要
机器人检测具有全面、准确、及时、可靠的特点,是最合适的桥梁斜拉索病害检测手段。通过论述机器人在桥梁斜拉索病害检测中的应用现状,对机器人的机械爬升系统、病害检测系统进行了分析,并展望了发展趋势。分析结果表明:轮式爬升方式存在夹持力过大的问题,且无法穿越斜拉索上的特殊障碍物;视觉检测系统可对光滑表面的斜拉索表观病害进行快速准确地自识别检测,但缺乏对非光滑表面斜拉索的自识别检测研究,且表观病害自识别模型还需改进;基于漏磁法的钢丝损伤检测系统则可实现对斜拉索内部钢丝损伤的定量化表征,但还缺乏统一、普适的漏磁检测评定指标,且缺乏能自动识别斜拉索钢丝损伤病害的模型。
Robot detection is the most suitable detection method for stay cable diseases because of its comprehensive, accurate, timely, and reliable characteristics. The application status of robot in cable disease detection was summarized, the mechanical climbing system and disease detection system of robot were analyzed, and the development trend was prospected. The analysis results show that the wheel climbing mode has the problem of excessive clamping force and cannot pass through the special obstacles on the stay cable. The visual inspection system can quickly and accurately self-identify and detect the apparent diseases of stay cables with smooth surfaces;however, there is a lack of research on self-identification detection of cables with non-smooth surfaces, and the self-identification model of apparent diseases needs to be improved. Cable wire damage detection system based on MFL can be used to quantitatively characterize the cable wire damage inside the stay cable, but there is still a lack of uniform and universal MFL evaluation index and a model that can automatically identify the cable wire damage.
作者
张洪
袁野
夏润川
周建庭
陈悦
ZHANG Hong;YUAN Ye;XIA Runchuan;ZHOU Jianting;CHEN Yue(State Key Laboratory of Bridge and Tunnel Engineering in Mountainous Areas Jointly Constructed bythe Ministry and the Province,Chongqing Jiaotong University,Chongqing 400074,China;School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2022年第12期62-69,共8页
Journal of Chongqing Jiaotong University(Natural Science)
基金
国家自然科学基金项目(52278291,U20A20314)
重庆市杰出青年科学基金项目(cstc2020jcyj-jqX0006)
重庆市自然科学基金项目(CSTB2022NSCQ-BHX0036,cstc2022ycjh-bgzxm0086)。
关键词
桥梁工程
斜拉索
病害检测
机器人
表观病害
钢丝损伤
bridge engineering
stay cable
disease detection
robot
surface diseases
cable wire damage
